Quality control, data cleaning, imputation
Methodology
2021-11-01 v1
Abstract
This chapter addresses important steps during the quality assurance and control of RWD, with particular emphasis on the identification and handling of missing values. A gentle introduction is provided on common statistical and machine learning methods for imputation. We discuss the main strengths and weaknesses of each method, and compare their performance in a literature review. We motivate why the imputation of RWD may require additional efforts to avoid bias, and highlight recent advances that account for informative missingness and repeated observations. Finally, we introduce alternative methods to address incomplete data without the need for imputation.
Cite
@article{arxiv.2110.15877,
title = {Quality control, data cleaning, imputation},
author = {Dawei Liu and Hanne I. Oberman and Johanna Muñoz and Jeroen Hoogland and Thomas P. A. Debray},
journal= {arXiv preprint arXiv:2110.15877},
year = {2021}
}
Comments
This is a preprint of a book chapter for Springer